13,313 research outputs found
MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions
Predicting interactions between structured entities lies at the core of
numerous tasks such as drug regimen and new material design. In recent years,
graph neural networks have become attractive. They represent structured
entities as graphs and then extract features from each individual graph using
graph convolution operations. However, these methods have some limitations: i)
their networks only extract features from a fix-sized subgraph structure (i.e.,
a fix-sized receptive field) of each node, and ignore features in substructures
of different sizes, and ii) features are extracted by considering each entity
independently, which may not effectively reflect the interaction between two
entities. To resolve these problems, we present MR-GNN, an end-to-end graph
neural network with the following features: i) it uses a multi-resolution based
architecture to extract node features from different neighborhoods of each
node, and, ii) it uses dual graph-state long short-term memory networks
(L-STMs) to summarize local features of each graph and extracts the interaction
features between pairwise graphs. Experiments conducted on real-world datasets
show that MR-GNN improves the prediction of state-of-the-art methods.Comment: Accepted by IJCAI 201
DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications
Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning
toolbox and have led to many breakthroughs in Artificial Intelligence. These
networks have mostly been developed for regular Euclidean domains such as those
supporting images, audio, or video. Because of their success, CNN-based methods
are becoming increasingly popular in Cosmology. Cosmological data often comes
as spherical maps, which make the use of the traditional CNNs more complicated.
The commonly used pixelization scheme for spherical maps is the Hierarchical
Equal Area isoLatitude Pixelisation (HEALPix). We present a spherical CNN for
analysis of full and partial HEALPix maps, which we call DeepSphere. The
spherical CNN is constructed by representing the sphere as a graph. Graphs are
versatile data structures that can act as a discrete representation of a
continuous manifold. Using the graph-based representation, we define many of
the standard CNN operations, such as convolution and pooling. With filters
restricted to being radial, our convolutions are equivariant to rotation on the
sphere, and DeepSphere can be made invariant or equivariant to rotation. This
way, DeepSphere is a special case of a graph CNN, tailored to the HEALPix
sampling of the sphere. This approach is computationally more efficient than
using spherical harmonics to perform convolutions. We demonstrate the method on
a classification problem of weak lensing mass maps from two cosmological models
and compare the performance of the CNN with that of two baseline classifiers.
The results show that the performance of DeepSphere is always superior or equal
to both of these baselines. For high noise levels and for data covering only a
smaller fraction of the sphere, DeepSphere achieves typically 10% better
classification accuracy than those baselines. Finally, we show how learned
filters can be visualized to introspect the neural network.Comment: arXiv admin note: text overlap with arXiv:astro-ph/0409513 by other
author
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